As the use of the Internet proliferates, it is increasingly being used as a vehicle for buying and selling of goods and services. The vendors who sell their goods or services on the Internet are often referred to as “online merchants.” These online merchants primarily use a website to advertise their products and services and secure orders from consumers for their offerings. For instance, a typical online merchant webpage may display relevant information about a product including but not limited to its price, sales tax, shipping and warranty information and details about the product. The information displayed on a website is often encoded in a digital form. Such information may be generally referred to as ‘digital information.’ One of the most difficult tasks for a consumer is to determine which online merchant is offering the best bargain for a particular product or service. To this effect, consumers often engage in bargain hunting where they may visit online merchants and bookmark the webpage displaying information about the product or service of interest to them. In some instances, the consumer may add some information e.g., metadata, of his own to each product web page so bookmarked. This activity of adding additional information is sometimes referred to as “annotation” or “tagging.”
The idea of tagging is not limited to textual information. Any type of digital information e.g., audio, video, graphics, etc. may be tagged. For example, a person watching or having watched a video online may provide annotation against the video or the location of the video.
However, the conventional techniques for gathering and analyzing such digital information often involves manual processing of the tags and annotations. For example, a webpage or a Web resource on the World Wide Web (“Web”) is most often identified by a Uniform Resource Identifier (URI). Other digital information available on a storage medium or a network may be identified by a “recall handle” similar to a URI. Like the URI, each recall handle is unique and is associated with only one item or page of information. Conventional techniques allow use of associating relevant keywords and phrases with a webpage, which is otherwise contextually uncertain. A user may be able to group contextually related web pages for later use. However, in some instances, web pages that share the same or similar annotation keywords and phrases may be grouped together even if they are contextually different. In addition, if the number of related web pages in a group lack specific information of interest to the user, it becomes difficult for the user to judge the relevancy of the web pages in light of the desired data to be analyzed. For example, a user may bookmark several web pages during his research, which he believes provide the information of his interest. The user may group these pages together as relating to the same item of interest. However, these bookmarked pages may not contain information that is actually relevant to what the user is searching. Subsequently, if the user attempts to extract relevant information from each of these bookmarked pages, he will have difficulty in evaluating the merits and relevancy of the information contained in the bookmarked web pages. The level of difficulty encountered by the user is directly proportional to the number of web pages being bookmarked.
Therefore, there is a need in the art for a method for efficient annotation of digital information.